Supply Chain & Logistics Analytics FAQs

Analytics unifies procurement, inventory, logistics and sell-out into one governed model so a question can cross the whole chain and get one answer. End-to-end visibility is a conformance achievement — agreeing shared keys across systems that were never designed to join — not a dashboard bought off the shelf.

A control tower turns a governed model into action: it watches for exceptions — a late inbound, stockout risk, an SLA breach — and alerts the owner. Start with three rules a named person acts on. The failure mode of every control tower is alerts with no owner, which get muted, after which the tower is decoration.

Each system lands in a lakehouse and is conformed to shared product, location, order and shipment keys so ERP, WMS and TMS finally join. The silo is not the systems; it is the absence of conformed keys between them, which the conformance layer exists to fix.

Power BI serves OTIF, fill rate, inventory health and supplier performance from a certified model on scheduled refresh, with drill-through to the failing orders. The value is a shared, trusted set of facts to decide against each week — provided the KPI definitions are agreed once, not argued per report.

OTIF, fill rate, order lead time and variance, days sales of inventory and turns, forecast accuracy and supplier on-time. Define each once with numerator, denominator and time base, and structure the hierarchy so operational detail rolls up to an executive view — decided once in the model, reused everywhere.

Analytics measures OTIF and fill rate against one agreed definition, then drills to root cause — a late inbound, a short pick, a planning gap — by supplier and site. The definition is the decision that matters: rescheduling the promise date silently improves OTIF while hiding the problem, so we fix the rule before the dashboard.

Models trained on clean demand history with seasonality and lead-time features improve short-term replenishment and longer-term S&OP forecasts, typically by 15–25%. The forecast is a planning aid that improves with clean history; most error is upstream data quality, which the model cannot fix.

Conform each warehouse feed to one item-and-location model and a 10-plus-site network reports from a single model instead of ten spreadsheets. The design decision is a clean location and item master; once that holds, adding a warehouse is a data feed, not a new report.

By modelling freight spend by lane, carrier and mode, analytics surfaces overpaid lanes, consolidation opportunities and mode-shift savings. It gives procurement evidence for the next negotiation and network review — the savings are real, and depend on invoices and rate cards being complete in the model.

Carrier scorecards rank on-time, damage, cost and compliance on a like-for-like basis across carriers, with alerts on breaches. The work is conforming carriers that report differently into one comparable model — that comparability is what turns an SLA review from anecdote into evidence.

Live shipment events feed the model (Eventstream where they genuinely stream, short pipelines where they batch), and exceptions — delays, missed milestones — surface as an actioned list with alerts to the owning coordinator. Match the latency to the decision; most control decisions need minutes, not seconds.

Telematics and GPS stream through an MQTT broker or IoT Hub into OneLake via Eventstream, joined to the vehicle master for utilisation and location analytics. The mapping between telematics IDs and the fleet master is what makes the numbers trustworthy; without an owner for it, utilisation drifts.

The model compares milestone times against SLA and surfaces breaches as a filtered exception list rather than a wall of green, with alerts to the coordinator. A consistent cross-carrier definition of "late" is the design decision that makes the exception list comparable and defensible.

A lakehouse lands ERP, WMS and TMS in Bronze and conforms the shared keys — product, location, order — in Silver that finally let them join. The silo is the missing shared key, not the systems; the Silver conformance layer is where the integration is really made.

MyData Insights builds supply chain and logistics analytics and control towers on Microsoft Fabric and Power BI, with Power Automate closing the loop from alert to action — practitioner-led, fixed-scope or Fractional Data Consultant, first value in six weeks. We diagnose the data and definitions before proposing the build.